AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset released by am-team in March 2025. The related paper results are "1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training". The dataset contains about 1.4 million data entries, covering various types of questions such as mathematics, code, scientific Q&A, and general chat. The data has been carefully selected, semantically deduplicated, and strictly cleaned to ensure the high quality and challenge of the data. Each entry in the dataset contains rich thinking traces, which not only provide examples of the reasoning process for the model, but also help the model better understand and generate complex reasoning task solutions. The release of the AM-DeepSeek-R1-Distilled-1.4M dataset aims to provide a powerful tool for the field of natural language processing and reasoning tasks, especially for training and optimizing the reasoning capabilities of large language models. It can help models improve their performance in key areas such as mathematics, code, and scientific Q&A, so as to better cope with various complex reasoning tasks.

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If you find our work helpful to your research, please give us a star :star: and cite our work :pencil: “BibTeX @misc{tian2025correctanswersequaldistillation, title={Not All Correct Answers Are Equal: Why Your Distillation Source Matters}, author={Xiaoyu Tian and Yunjie Ji and Haotian Wang and Shuaiting Chen and Sitong Zhao and Yiping Peng and Han Zhao and Xiangang Li}, year={2025}, eprint={2505.14464}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.14464}, } @misc{ji2025amthinkingv1advancingfrontierreasoning, title={AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale}, author={Yunjie Ji and Xiaoyu Tian and Sitong Zhao and Haotian Wang and Shuaiting Chen and Yiping Peng and Han Zhao and Xiangang Li}, year={2025}, eprint={2505.08311}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.08311}, } @misc{tian2025exploringpotentialofflinerl, title={Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study}, author={Xiaoyu Tian and Sitong Zhao and Haotian Wang and Shuaiting Chen and Yiping Peng and Yunjie Ji and Han Zhao and Xiangang Li}, year={2025}, eprint={2505.02142}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.02142}, } @misc{tian2025deepdistillenhancingllmreasoning, title={DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training}, author={Xiaoyu Tian and Sitong Zhao and Haotian Wang and Shuaiting Chen and Yiping Peng and Yunjie Ji and Han Zhao and Xiangang Li}, year={2025}, eprint={2504.17565}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.17565}, } @misc{wang2025leveragingreasoningmodelanswers, title={Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability}, author={Haotian Wang and Han Zhao and Shuaiting Chen and Xiaoyu Tian and Sitong Zhao and Yunjie Ji and Yiping Peng and Xiangang Li}, year={2025}, eprint={2504.09639}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.09639}, } @misc{ji2025difficultyawarestagedreinforcementlearning, title={How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study}, author={Yunjie Ji and Sitong Zhao and Xiaoyu Tian and Haotian Wang and Shuaiting Chen and Yiping Peng and Han Zhao and Xiangang Li}, year={2025}, eprint={2504.00829}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.00829}, } @misc{tian2025thinktwiceenhancingllm, title={Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking}, author={Xiaoyu Tian and Sitong Zhao and Haotian Wang and Shuaiting Chen and Yunjie Ji and Yiping Peng and Han Zhao and Xiangang Li}, year={2025}, eprint={2503.19855}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.19855}, } @misc{zhao202514millionopensourcedistilled, title={1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training}, author={Han Zhao and Haotian Wang and Yiping Peng and Sitong Zhao and Xiaoyu Tian and Shuaiting Chen and Yunjie Ji and Xiangang Li}, year={2025}, eprint={2503.19633}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.19633}, } “